Electrocardiogram-based assessment of diastolic function

ABSTRACT

Diastolic function may be assessed by operating one or more machine-learned computational models on electrocardiograms or electrocardiogram-derived features to compute quantitative diastolic indicators, including estimates of echocardiography parameters conventionally measured by echocardiography. In various embodiments, parameters derived from time-frequency transforms of the electrocardiograms are used as input to the model(s) and/or computed within the model(s).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Applications No. 62/894,598, filed on Aug. 30, 2019, and No.63/065,837, filed on Aug. 14, 2020, both entitled “Left VentricularRelaxation Risk Stratification.” Both priority applications are herebyincorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to heart testing, and more particularlyto systems, devices, and methods for determining quantitative and/orqualitative indicators of diastolic function.

BACKGROUND

Heart testing for coronary heart disease, myocardial ischemia, and otherabnormal heart conditions is routinely performed using anelectrocardiogram (ECG), which represents electrical potentialsreflecting the electrical activity of the heart measured via electrodesplaced on the patient's skin. The heart's electrical system controlstiming of the heartbeat by sending an electrical signal through thecells of the heart. The heart includes conducting cells for carrying theheart's electrical signal, and muscle cells that contract the chambersof the heart as triggered by the heart's electrical signal. Theelectrical signal starts in a group of cells at the top of the heartcalled the sinoatrial (SA) node. The signal then travels down throughthe heart, conducting cell to conducting cell, triggering first the twoatria and then the two ventricles. Simplified, each heartbeat occurs bythe SA node sending out an electrical impulse. The impulse travelsthrough the upper heart chambers, called “atria,” electricallydepolarizing the atria and causing them to contract. Theatrioventricular (AV) node of the heart, located on the interatrialseptum close to the tricuspid valve, sends an impulse into the lowerchambers of the heart, called “ventricles,” via the His-Purkinje system,causing depolarization and contraction of the ventricles. Following thesubsequent repolarization of the ventricles, the SA node sends anothersignal to the atria to contract, restarting the cycle. This pattern andvariations therein indicative of disease are detectable in an ECG, andallow medically trained personnel to draw inferences about the heart'scondition. However, not every developing abnormality is immediatelyvisible in an ECG, and, consequently, many patients are misdiagnosed ashealthy.

A complementary test sometimes performed to evaluate heart condition isa transthoracic echocardiogram, which uses ultrasound to obtain imagesof the heart's valves and chambers and enables ascertaining metrics ofheart movements to quantitatively assess pumping action. These metricsinclude, for instance, the mitral annular velocities and the transmitralflow velocities during early and late diastole. As the ventriclerelaxes, the mitral annulus (a ring-like structure that separates theleft atrium from the left ventricle) moves towards the base of theheart, signifying the volume expansion of the ventricle. The peak earlydiastolic mitral annular velocity, e′, measured during early filling, isa metric of left ventricular diastolic function, and has been shown tobe relatively independent of left ventricular filling pressure. In caseof impaired relaxation (diastolic dysfunction), e′ decreases. The peaklate diastolic mitral annular velocity, a′, which is measured after theearly relaxation when the ventricular myocardium is passive, is a metricof atrial contraction, and may likewise serve to quantify diastolicfunction. In addition to the absolute values of e′ and a′, theearly/late ratio between e′ and a′ can be another useful quantitativeindicator. Further, during the two filling phases, there is early (E)and late (A) blood flow from the atrium to the ventricle, correspondingto the annular velocity phases. The flow is driven by the pressuredifference between atrium and ventricle, and this pressure difference isa function of both the pressure drop during early relaxation and theinitial atrial pressure. With minor diastolic dysfunction, the peakearly diastolic transmitral flow velocity, E, is reduced in proportionto e′, but if relaxation is reduced to an extent that it causes anincrease in atrial pressure, E will increase again, while e′, being lessload-dependent, remains low. Thus, the ratio E/e′ is related to theatrial pressure, and can indicate increased filling pressure (althoughwith several reservations). In the right ventricle, this is not animportant principle, as the right atrial pressure is the same as centralvenous pressure, which can easily be assessed from venous congestion.

Echocardiograms are currently the gold standard to diagnose diastolicdysfunction, but, at typical costs on the order of $200, they areexpensive compared, e.g., with ECGs (which cost on the order of $50),and are therefore generally only used once a problem with heartfunction, such as a strong heart murmur or a symptom like chest pain oran irregular heartbeat, has been observed.

SUMMARY

Described herein is an approach to estimating indicators of diastolicfunction or dysfunction (herein also “diastolic indicators”) based onECG measurements, which enhances the utility of ECGs and obviates, inmany circumstances, the need for an additional costly echocardiogram. Invarious embodiments, one or more machine-learned computational modelstrained in a supervised manner on ECG measurements correlated withparameters obtained by echocardiography (hereinafter “echocardiogramparameters”) (such as, e.g., e′, a′, E, A, and ratios) that serve as theground truth, operate on ECG-derived features, optionally in conjunctionwith patient demographic parameters (e.g., age, sex, etc.), to computequantitative estimates of the echocardiogram parameters and/or otherindicators of diastolic function. In some embodiments, theechocardiogram parameter estimates are provided as input to anotherlayer of machine-learned computational models, or are otherwiseprocessed, to compute one or more quantitative indices or scores (e.g.,a left ventricular relaxation risk score, a lateral left ventricularrelaxation index, a septal left ventricular relaxation index, or acomposite left ventricular relaxation index) and/or to classifyindividuals' diastolic function categorically (e.g., distinguishingbetween normal, abnormal, or borderline function). The computationalmodel(s) and outputs can be validated against a reference studypopulation to determine associated statistical indicators such asprevalence probability, relative risk, likelihood ratios, or confidenceintervals for predicted ranges of clinically measurable attributes,which aid risk stratification (e.g., the separation of a population intohigh-risk, low-risk, and rising-risk groups) and ultimately allowmedical personnel to interpret the model outputs to render diagnoses forindividual patients. By basing classifications, risk scores, etc., onquantitative estimates of echocardiogram parameters, which constitutemetrics of diastolic dysfunction that are familiar to physicians, thedescribed approach provides a more transparent and more granulardiagnostic tool than, e.g., a machine-learned model that directlyoutputs a categorical assessment of diastolic function.

In accordance with various embodiments, the ECG-derived features used asinput to the computational model(s) include time-frequency featuresderived using discrete or continuous wavelet transforms (or othertime-frequency transforms) of the ECGs. Conventional ECG parametersderived directly from the time-domain ECGs, such as, e.g.,Glasgow-derived parameters, may be used as additional, time-domain inputfeatures to the model. Supervised training of the computational model(s)may utilize training data that includes the ECG-derived time-frequencyand time-domain features as input features, along with the ground-truthechocardiogram parameters as output labels. Alternatively, thetime-frequency transform may itself be computed by a neural networkmodel, whose output flows into neural networks implementing thecomputation model(s) for computing the echocardiogram parameterestimates and/or other diastolic indicators. In this case, a multi-levelneural network system including a neural network for computingtime-frequency transforms at the first level and one or more neuralnetworks for computing echocardiogram parameter estimates and optionallyadditional diastolic indicators at one or more subsequent levels may betrained based on training data that includes the raw ECGs (along withpatient demographic parameters) as input features, labeled by theground-truth echocardiogram parameters. Beneficially, the use oftime-frequency ECG features (optionally in combination with time-domainECG features and/or patient demographic parameters), whether providedexplicitly as input to a computational model or computed within a levelof a multi-level computational model, can increase the accuracy of theobtained echocardiogram parameter estimates, as compared with modelswhose ECG-derived input is limited to time-domain features.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system for quantifying diastolicfunction, in accordance with various embodiments.

FIG. 2 is a flow chart of an example method for quantifying diastolicfunction using echocardiogram parameter estimates computed fromelectrocardiograms, in accordance with various embodiments.

FIG. 3 shows plots of example lateral left ventricular relaxation indexdistributions for normal and abnormal patient populations, illustratingan example diastolic indicator useful for risk stratification inaccordance with one embodiment.

FIG. 4 is a data flow diagram illustrating an example two-levelcomputational model architecture for computing echocardiogram parameterestimates, in accordance with various embodiments.

FIG. 5 is a data flow diagram illustrating an example two-levelcomputational model architecture for computing various diastolicindicators using echocardiogram parameter estimates, in accordance withvarious embodiments.

FIG. 6 is a data flow diagram illustrating an example two-level neuralnetwork architecture 600 for computing diastolic indicators from rawelectrocardiograms using an implicit time-frequency transform, inaccordance with various embodiments.

FIG. 7 is a user interface showing an example report screen for anabnormal case in accordance with various embodiments.

FIG. 8 is a user interface showing an example report screen for aborderline case in accordance with various embodiments.

FIG. 9 is a block diagram of an example computer system as may serve asprocessing facility in accordance with various embodiments.

DESCRIPTION

The approach described herein combines conventional ECG hardwareproducing one or more leads, e.g., 10 electrodes and associatedcircuitry for a standard 12-lead ECG, with new processing functionalityto derive indicators of diastolic (dys-)function. In variousembodiments, the processing functionality implements one or morecomputational models that operate on ECGs or ECG-derived features, suchas Glasgow-derived parameters and/or parameters derived using wavelet orother time-frequency transforms (e.g., short-time Fourier transform) ofthe ECGs, along with patient demographic parameters, and outputindicators of diastolic (dys-)function, including quantitative estimatesof one or more echocardiogram parameters. Beneficially, estimating theechocardiogram parameters based on features derived from ECGs (includingby time-frequency transform), optionally in conjunction with patientdemographic parameters, provides a cost-efficient alternative tomeasuring the echocardiogram parameters via electrocardiography, as isdone conventionally.

FIG. 1 is a block diagram of an example system 100 for quantifyingdiastolic function, in accordance with various embodiments. The system100 includes one or more electrodes 102 for acquiring ECG signals (e.g.,10 electrodes for a traditional 12-lead ECG), a processing facility 104for processing the ECG signals, and an electrode interface 106connecting the electrodes 102 to the processing facility 104. Theelectrode interface 106 includes circuitry that outputs electricalsignals suitable as input to the processing facility 104, e.g., bydigitally sampling analog input signals. The system 100 further includesa display device 108 for outputting the ECG test results (including,e.g., the ECGs themselves, time-frequency maps computed therefrom,and/or various quantitative and/or qualitative indicators of diastolicfunction computed as described herein), and optionally otherinput/output devices 109, such as a keyboard and mouse and/or a printer,for instance. The display device 108 may be a touchscreen doubling as auser-input device.

The processing facility 104, electrode interface 106, display 108, andinput/output devices 109 may be implemented as a single, stand-alonedevice implementing all computational functionality for ECG signalprocessing and presentation. Alternatively, they may be provided by acombination of multiple devices. For example, an ECG test device withlimited functionality for recording and/or processing ECG signalsreceived from one or more electrodes 102 via an electrode interface 106of the device may outsource certain computationally intense processingtasks to one or more other computers. Data exchange between the ECG testdevice and the other computer(s) may take place via a wired or wirelessnetwork. For example, the ECG test device may be connected via theinternet to a cloud-based signal-processing service. that receives theECGs in near real time as they are being measured, or at a later time.Alternatively, the measured ECGs may be stored on a removablecomputer-readable medium that is subsequently read by another computerfor processing. Thus, the functionality of the processing facility 104may be distributed between multiple computational devices. Whetherprovided in a single device or distributed, the processing facility 104may be implemented with a suitable combination of hardware and/orsoftware, such as a suitably programmed general-purpose computer(including at least one central processing unit (CPU) or graphicprocessing unit (GPU) and associated memory); dedicated, special-purposecircuitry (such as, e.g., a digital signal processor (DSP),field-programmable gate array (FPGA), analog circuitry, or other); or acombination of both. Herein, the term “hardware processor” is used inreference to both special-purpose circuitry and general-purposeprocessors as used in general-purpose computers and configured viasoftware.

The processing facility 104 may include various functionally distinctcomponents, such as separate computer programs or functions calledwithin a larger program flow, and/or special-purpose circuitry forcertain computational tasks. These components may include an ECG-signalprocessing component 110 that generates ECGs for multiple leads from the(e.g., digitally sampled) ECG signals for display and analysis (e.g., byfiltering, smoothing, scaling, etc., as well as by combining signals forvarious leads); a time-frequency transform component 112 that convertsthe ECG for each lead into a two-dimensional time-frequency map (signedor unsigned) and, optionally, normalizes the time-frequency map; afeaturizer 114 that computes and extracts relevant parameters from theECGs and/or the time-frequency maps for use as input features to one ormore machine-learned models 116; the one or more machine-learnedcomputational models 116, which determine echocardiogram parameterestimates and/or other diastolic indicators from these ECG-derivedfeatures in conjunction with patient demographic parameters; and/or auser-interface 118 component that generates graphic representations ofthe data provided by the other modules and assembles them into a screenfor display (as shown, e.g., in FIGS. 9 and 10). TheECG-signal-processing component 110 may be a conventional processingmodule as used in commercially available heart monitors and/or as iscapable of straightforward implementation by one of ordinary skill inthe art. The time-frequency transform component 112, featurizer 114, andcomputational models 116 implement algorithms and provide functionalityexplained further below, and can be readily implemented by one ofordinary skill in the art given the benefit of the present disclosure.

For purposes of the creating the machine-learned models 116, someinstances of the processing facility 104 also include a training engine120 that implements one or more suitable machine-learning algorithms tobuild and/or train (that is, determine adjustable parameters of) themodels 116 based on training data. Suitable training algorithms forvarious types of models are well-known to those of ordinary skill in theart. For example, a neural network model may be trained usingbackpropagation of errors, e.g., with stochastic gradient descent. Notethat, once the machine-learned models 116 have been trained and theirparameters are fixed, the training engine 120 is no longer used;accordingly, the training engine 120 may be omitted from a processingfacility 104 configured for assessing diastolic function of patients inthe inference phase.

As will be readily appreciated, the depicted components reflect merelyone among several different possibilities for organizing the overallcomputational functionality of the processing facility 104. Thecomponents may, of course, be further partitioned, combined, or alteredto distribute the functionality differently. The various components maybe implemented as hardware components, software components (e.g.,executed by a general-purpose processor), or a combination of both. Forexample, it is conceivable to implement the time-frequency transformcomponent 112 (which generally involves the same operations for eachincoming ECG signal) and/or certain machine-learned computational models116 with special-purpose circuitry to optimize performance, whileimplementing other components in software.

FIG. 2 is a flow chart of an example method 200 for quantifyingdiastolic function using echocardiogram parameter estimates computedfrom electrocardiograms, in accordance with various embodiments. Themethod 200 begins, in acts 102 and 104, with measuring one or more ECGsfor a patient (act 102) and recording relevant patient demographicparameters (such as, e.g., age, sex, height, weight, health conditionssuch as hypertension, or other factors that might affect how thepatient's diastolic functions is reflected in ECGs and parametersderived from ECG) (act 204). The term “ECG” in the singular is hereingenerally used in reference to an individual lead; and multiple ECGsmeasured for a patient may, accordingly, correspond to multiplerespective leads. For example, from ECG signals measured with tenelectrodes in a standard configuration, ECGs for twelve leads can becomputed in a manner routinely used in the medical arts. It is to beunderstood, however, that the method described herein does not requirethe use of all twelve leads, but is generally applicable to any numberof ECGs, such as any subset of the standard twelve leads.

In accordance with the standard configuration, four of the tenelectrodes (conventionally labeled LA, RA, LL, RL) are placed on thepatient's left and right arms and legs; two electrodes (labeled V1 andV2) are placed between the fourth and fifth ribs on the left and rightside of the sternum; a further, single electrode (labeled V3) is placedbetween V2 and V4 on the fourth intercostal space; one electrode(labeled V4) is placed between the fifth and sixth ribs at themid-clavicular line (the imaginary reference line that extends down fromthe middle of the clavicle), and, in line therewith, another electrode(labeled V5) is positioned in the anterior axillary line (the imaginaryreference line running southward from the point where the collarbone andarm meet), and the tenth electrode (labeled V6) is placed on the samehorizontal line as these two, but oriented along the mid-axillary line(the imaginary reference point straight down from the patient's armpit).The electric potentials measured by electrodes V1 through V6 correspondto six of the twelve standard leads; the remaining six leads correspondto the following combinations of the signals measured with theindividual electrodes: I=LA−RA; II=LL−RA; III=LL−LA; aVR=RA−½ (LA+LL);aVL=LA−½ (RA+LL); and aVF=LL−½ (RA+LA).

With reference again to FIG. 2, the method 200 further involvestransforming the ECGs (immediately upon measurement, or at a later timeafter storage in memory) into two-dimensional time-frequency maps by asuitable mathematical transform, such as, for instance, wavelettransform or short time Fourier transform, in act 206. In certainembodiments, a continuous wavelet transform (CWT) is used. For a givencontinuous ECG x(t), the CWT is given by:

${{W( {a,b} )} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{\psi ( \frac{t - b}{a} )}{x(t)}{dt}}}}},$

where ψ is a selected wavelet, b corresponds to a shifted position intime and a to a scaling factor, and W(a, b) is the two-dimensionalfunction of position in time and scale resulting from the transform,also called wavelet coefficients. Similarly, for a discretized ECG x(k)(where k is an integer), the CWT is given by:

${{W( {a,b} )} = {\frac{1}{\sqrt{a}}{\sum_{k}{{x(k)}( {{\int_{- \infty}^{{({k + 1})}T}{\overset{\_}{\psi ( \frac{t - b}{a} )}{dt}}}\  - \ {\int_{- \infty}^{kT}{\overset{\_}{\psi ( \frac{t - b}{a} )}{dt}}}} )}}}},$

where T is the sampling period. The wavelet selected for processing maybe, for example, a Mexican hat wavelet, Morlet wavelet, Meyer wavelet,Shannon wavelet, Spline wavelet, or other wavelet known to those ofordinary skill in the art. The CWT W(a, b) is also referred to as ascalogram. The time-frequency maps (such as, e.g., scalograms) generallyinclude both positive and negative values, i.e., they are “signed.” Insome embodiments, the absolute value of the signal value (or the squareof the signal value) is taken at each time-frequency point, resulting inan “unsigned” time-frequency map.

The ECGs and time-frequency maps are processed, in act 208, to extractparameters to be provided, along with the patient demographicparameters, as input features to one or more machine-learnedcomputational models. Time-domain features extracted directly from theECGs may include, for example (and without limitation), the extrema,durations, or area of any of the P, Q, R, S, or T waves, or signalamplitudes at one or more specified points in time associated with theP, Q, R, S, or T waves. The input to the models may also includeconventional ECG-derived parameters, such as Glasgow parameters.Time-frequency features extracted from the time-frequency maps mayinclude any or all of the time-frequency coefficients (e.g., waveletcoefficients) themselves or any parameters derived from thetime-frequency maps, for example (and without limitation), extremaacross frequency at one or more points in time associated with the P, Q,R, S, or T waves, extrema across time at one or more specified points infrequency, or integral measures associated with extrema in thetime-frequency map. U.S. Pat. No. 9,700,226, filed on Sep. 20, 2016,which is incorporated herein by reference, describes time-frequencytransforms of ECGs (in particular CWTs) and various parameters derivedfrom the resulting time-frequency maps that may be used as inputfeatures. In general, time-frequency features may be extracted from thesigned or unsigned time-frequency map or a combination of both. Further,both the time-domain and the time-frequency features may includeparameters derived from individual heartbeats across synchronized ECGsfor different leads and from beat to beat. For example, a singleparameter derived from the T wave may be obtained for each lead and formultiple heartbeats. In this example, using twelve leads and twentyrecorded heartbeats, such a single parameter would yield 12.20=240features. An additional single parameter obtained for the R wave wouldbecome another 240 features, etc. A set of features corresponding to thesame parameter measured across multiple leads captures the phasedifferences between leads, which can provide important comparisonsuseful as input to the computational models. Values of a singleparameter measured over multiple heartbeats may be combined into asingle aggregated feature, e.g., the average or median across themultiple heartbeats.

In act 210, one or more machine-learned computational models (e.g.,implemented by component 116 of system 100) operate on the ECG-derivedfeatures (including the time-frequency features) and patient demographicparameters to compute one or more diastolic indicators, including one ormore estimates of echocardiogram parameters (e.g., e′, a′, E, A, andratios). Multiple computational models (potentially of different types)may be used to compute different respective parameter estimates andindicators. In various embodiments, multiple computational models areorganized in a one-level structure or, alternatively, in a hierarchy oftwo or more levels. FIG. 4 (described below) provides an example of atwo-level hierarchy. Further, individual indicators may be computed witha multi-level hierarchy of models, the number of models and/or levelsgenerally being different for each indicator. In a two-level hierarchyfor an individual indicator, different models may, for instance, be usedto compute a first set of estimates, and an ensemble model may then beused at the second level to combine the first set of estimates into afinal estimate. The estimates of the echocardiogram parameters arecontinuous-variable estimates that may be computed with regressionmodels or may, alternatively, be piecewise approximated usingclassification models. In general, the computational model(s) mayinclude, e.g., decision trees or random forests, neural networks,regression analysis, Bayesian networks, or other suitablemachine-learning models known to those of ordinary skill in the art.Regression models that were found, in some embodiments, to provideparticularly good performance in estimating the echocardiogramparameters include random forest and least squares models.

The echocardiogram parameter estimate(s) output by the computationalmodel(s) may flow as inputs into further algorithms and/or additionalmachine-learned computational models for computing, in act 212,additional diastolic indicators, such as qualitative classifications ofdiastolic function (e.g., a three-level classification between normal,borderline, and abnormal diastolic function; or a 5-level classificationbetween low-possibility, possible, borderline, probable, and highlyprobable LV relaxation abnormality), and/or quantitative indicators suchas left ventricular (LV) relaxation risk score(s), LV relaxation indices(e.g. lateral, septal, and/or average/composite indices, etc.). Ifmachine-learned computational models are used for this purpose, thesemodels form, along with the computational models for computing theechocardiogram parameter estimates in act 210, a two-level hierarchicalarchitecture (optionally with sub-levels at one or both levels), e.g.,as illustrated by way of example in FIG. 5 (described below). Further,in some embodiments, certain qualitative and/or quantitative diastolicindicators (such as classifications or LV relaxation indices) arecomputed along with the echocardiogram parameter estimates by the (firstlevel of) computational models in act 210, and flow, along with theechocardiogram parameter estimates, into downstream computations.

The computation of additional diastolic indicators in act 212 need notnecessarily utilize machine-learned models. For example, echocardiogramparameter estimates may be scaled to indices spanning a fixed range(e.g., from 0 to 100). As another example, echocardiogram parameterestimates may be compared against specified thresholds to classifydiastolic function between various degrees or likelihoods ofabnormality. For example, one measure of left ventricular diastolicdysfunction is low e′. (See “Recommendations for the Evaluation of LeftVentricular Diastolic Function by Echocardiography: An Update from theAmerican Society of Echocardiography and the European Association ofCardiovascular Imaging,” J Am Soc Echocardiogr 2016; 29:277-314.) Low e′is defined as septal e′ velocity <7 cm/s or lateral e′ velocity <10cm/s, where septal e′ is the velocity of the septal mitral annularmotion at early diastole and lateral e′ is the velocity of the lateralmitral annular motion. Ordinarily, septal and lateral e′ are measuredvia transthoracic echocardiography, but they can be estimated, inaccordance herewith, from ECGs. The estimated septal and lateral e′parameters may be compared against values of 7 or 10 (in cm/s),respectively, to classify diastolic function as normal or abnormal.Optionally, the threshold values for comparison against the estimated e′parameters may be set lower or higher than those used with theparameters obtained by echocardiography to account for any error in theestimate and provide higher confidence for normal and abnormalclassifications, with a region of uncertainty in between.

The echocardiogram parameter estimates and/or other diastolic indicatorscomputed in acts 210, 212 may be provided as output (e.g., displayed onscreen, printed, sent via electronic notification, etc.) to a physicianor other clinical personnel in act 214, optionally along with the ECGsand/or time-frequency maps from which these indicators have beencomputed. FIGS. 9 and 10 provide example output displays.

To aid interpretation of the diastolic indicators, statistical metricsdetermined by validating the diastolic indicators against a referencestudy population may be provided. Such metrics may include, e.g.,statistical indicators such as prevalence probability, relative risk,likelihood ratios, or confidence intervals for predicted ranges ofclinically measurable attributes.

Prevalence probability is a measure of the probability that a personwith a certain test result has a certain condition, determined using areference study population, and can be calculated by dividing the numberof persons with the same test results that have the condition in thereference study population by the total number of persons with the sametest result in the reference study population.

Relative risk is a ratio of the probability of an event occurring in aparticular sub-group (Group A) of a population versus the probability ofthe event occurring in a reference sub-group (Group B) of the samepopulation that is independent of the sub-group being studied (i.e.,Group A and Group B are independent sub-groups within the samepopulation), and can be calculated, accordingly, by dividing theprobability of the event in Group A by the probability of the event inGroup B.

Likelihood ratios (LR) in medical testing are used to interpretdiagnostic tests by indicating how likely a patient has a disease orcondition. The higher the ratio, the more likely the patient has thedisease or condition. Conversely, a low ratio means that the patientvery likely does not have the disease or condition. Therefore, theseratios can help a physician rule in or rule out a disease. A likelihoodratio is calculated by dividing the probability that a person with thecondition has a certain test result by the probability that a personwithout the condition has that test result.

Confidence intervals demonstrate a range of values that a predictedmeasure (such as, e.g., a diastolic indicator) may actually fall betweenwith a certain degree of confidence. Typically, if a dataset follows anormal distribution, then these intervals are calculated usingstatistical techniques based on formulas that are widely accepted fornormally distributed datasets. The most common confidence intervals usedin medicine are 95% and 70% confidence intervals since they are easilycalculated using a mean and standard deviation derived from the overalldataset. Confidence intervals are used in risk-stratification byexamining how much of the overall interval lies in the clinicallyaccepted normal or abnormal range. Confidence intervals can also be usedfor rule-in or rule-out assessments if the 95% confidence interval liescompletely to one side or the other of a clinically accepted threshold.A 70% confidence interval corresponds to the population mean+/−thepopulation standard deviation. A 95% confidence interval corresponds tothe population mean+/−twice the population standard deviation.

FIG. 3 shows example distribution plots 300, 302 of the lateral LVrelaxation index (along the independent axis) for “normal” and“abnormal” patient populations, respectively, based on calculatedaverages and standard deviations. The plots demonstrate that the lateralLV relaxation index, indeed, has risk-stratification value, especiallyfor normal patients. The abnormal plot 302 overlaps significantly withthe normal plot 300 (by about 30% of the normal plot), but the plots300, 302 can nonetheless be used to calculate both percentiledifferences and likelihood ratios. While FIG. 3 shows distribution plotsfor, specifically, the lateral LV relaxation index, other LV relaxationindices exhibit similar behavior.

FIG. 4 is a data flow diagram illustrating an example two-levelcomputational model architecture 400 for computing echocardiogramparameter estimates, in accordance with various embodiments. In thisfigure, as well as in FIGS. 5 and 6 explained below, rectanglesgenerally indicate computational blocks, while ovals generally indicatedata provided as input to or resulting as output from these blocks. Thetwo-level architecture 400 takes a combination of conventional and/orother time-domain ECG features 402, time-frequency features 404 selectedor derived from time-frequency transform(s) of the ECG(s), and patientdemographic parameters 406 as input into multiple machine-learnedcomputational models 408, 410, 412, 414 (“ML models”) at the first level416 of the hierarchy 400. Different models may operate on differentsubsets of the input features 402, 404, 406. Outputs of the first-levelmodels 408, 410, 412, 414 flow as inputs into respective ensemble models418, 420, 422, 424 at the second level 426 of the hierarchy. Theensemble models 418, 420, 422, 424 each compute one of various diastolicindicators from the first-level outputs (optionally augmented by some orall of the first-level feature inputs 402, 404, 406). For example, asshown, a number of computational models 408 (which generally includemodels of different types, e.g., random-forest, least-squares, andneural network models) may provide first-level estimates of lateral e′,which are then further processed, in the associated ensemblemodel/algorithm 418, to compute a final estimate 430 of lateral e′.Combining the results of multiple computational models in this mannermay achieve higher-accuracy estimates than one model alone. Similarly,other echocardiogram parameter estimates 432, 434, e.g., as shown, forseptal e′ and a′, can be computed by two levels of computational andensemble models (e.g., models 410, 422 to compute septal e′ 432 andmodels 412, 422 to compute a′ 434). Further, as shown, the two-levelarchitecture 400 may include models 414, 424 to compute other diastolicindicators, e.g., as shown, a composite LV relaxation index 436,directly from the inputs 402, 404, 406.

In general, the first-level computational models 408, 410, 412, 414 mayinclude binary (2-class), multi-class (e.g., 3-class or 5-class), orregression (continuous-variable) models, and the second-levelcomputational models may include ensemble models 418, 420, 424, 426.Each ensemble model 418, 420, 424, 426, in turn, may include one or morebinary, multi-class, and/or regression models, optionally augmented bylogic, equations, formulas, or algorithms to further refine the accuracyof the first-level models.

FIG. 5 is a data flow diagram illustrating an example two-levelcomputational model architecture 500 for computing various diastolicindicators using echocardiogram parameter estimates, in accordance withvarious embodiments. Herein, machine-learned computational models 502,504, 506, and optionally 508 operate on the ECG-derived features 402,404 and patient demographic parameters 406 to generate echocardiogramparameter estimates 510, 512, 514 (e.g., as shown, lateral and septale′, as well as a′) and optionally other diastolic indicators 516 at theoutput of the first level 518. These echocardiogram parameter estimates510, 512, 514 and/or other diastolic indicators 516 are then provided asinput to a second level 520 of machine-learned computational models 522,524, which compute various downstream categorical or quantitativediastolic indicators, e.g., as shown, a normal/borderline/abnormalclassification 526 or risk score 528. Different selections andcombinations of the first-level outputs may be relevant for differentones of the downstream diastolic indicators.

As will be appreciated by those of ordinary skill in the art, the levels518, 520 for computing first-level echocardiogram parameter estimates510, 512, 514 and downstream second-level diastolic indicators 526, 528,respectively, may each include multiple sub-levels. For example, thearchitectures 400, 500 can be used in combination, such that each of thecomputational models 502, 504, 506, 508 for computing, e.g.,echocardiogram parameter estimates is implemented by two sub-levelscorresponding to models of the levels 416, 426 of the architecture 400.Thus, the model 502 for computing lateral e′, for instance, may includea two-level structure of models 408, 418.

The one or more models (e.g., model architectures 400, 500) may bedeveloped using machine-learning training processes, such as supervisedlearning. Training data to be used in the training process may beobtained from a (generally large) number of patients whose diastolicfunction spans a range from normal function to a high degree of abnormal(or dys-) function. For each patient, both one or more ECGs and one ormore echocardiograms are acquired. The ECGs are processed to deriveconventional/time-domain as well as time-frequency parameters to be usedas input features to the model(s), and the echocardiograms are processedto derive one or more echocardiogram parameters of interest that willserve as the ground truth for training. The training dataset includespairs of a set of input features and a set of output labels for eachpatient, the input features including the ECG-derived parameters as wellas any relevant patient demographic parameters, and the output labelsincluding the echocardiogram parameters for that patient. To train amodel (e.g., one of the models 408, 410, 412) to estimate a givenechocardiogram parameter, the input features are fed into the model, andthe model-generated output is compared against the ground-truthechocardiogram parameter; the discrepancy between the measured(ground-truth) and estimated echocardiogram parameters is used asfeedback to iteratively adjust the model. To directly train a model foranother type of diastolic indicator, such as a classification or riskscore, ground-truth classifications or scores may be determined for eachpatient, e.g., from the measured echocardiogram parameters. Trainingalgorithms for building and training various types of models arewell-known to those of ordinary skill in the art.

Model development may also incorporate various feature-reductiontechniques, such as hyper-parameter tuning, random forest featureimportance, de-correlation, principal component analysis (PCA),clustering, minimum redundancy maximum relevance, mean-0 normalization,min-max normalization, etc. Feature-reduction analysis is done duringthe model training process for each model individually to select themost useful features for that model and discard the others which havelower predictive qualities for the target value of the model (which isthe parameter that the model is intended to predict). For example, thetraining process may initially operate on hundreds of input features,whose relative contributions to accurately predict the target parameterare quantitatively assessed to reduce the input feature set to merelytens of features. Feature reduction performed during training of variousmodels has shown that, in general, time-frequency features contributesignificantly to the accuracy of the echocardiogram parameter estimates.In some embodiments, the feature set retained at the completion offeature-reduction analysis includes at least one third time-frequencyfeatures, and less than 20% patient-demographic features, the remainderbeing traditional ECG features. In one example embodiment, a set ofeighteen features selected as input to a model for estimating e′includes about 39% time-frequency features.

So far, it has been assumed that the time-frequency features flowinginto the computation of echocardiogram parameter estimates and/or otherdiastolic indicators are computed explicitly and provided to themachine-learned computational model(s) as input features. Alternatively,it is possible to utilize time-frequency information implicitly bystructuring the models in a manner that extracts relevant time-frequencyparameters. For example, the computational models may be implemented asa multi-level neural network system including a neural network forcomputing a time-frequency transform at the first level and one or moreneural networks for computing echocardiogram parameter estimates andoptionally additional diastolic indicators at the subsequent secondlevel (which, as explained above, may itself include multiple(sub-)levels).

FIG. 6 is a data flow diagram illustrating an example two-level neuralnetwork architecture 600 for computing diastolic indicators from one ormore raw ECGs 602, using an implicit time-frequency transform, inaccordance with various embodiments. At the first level, a convolutionalneural network (CNN) 604 may be configured to compute a time-frequencytransform 606 for each ECG 602. For example, to compute the CWT of anECG 602, the weights of the CNN 604 may be chosen to be the wavelets ψevaluated across a range of different values of the scaling factor α. Inthe case of a 8192-sample ECG with 64 CWT scales α, there are 8192inputs to the convolutional layer and 64 rows of 8192 neurons for atotal of 8192.64=524288 outputs, which constitute the waveletcoefficients W(a, b), that is, the outputs 606 of the time-frequencytransform (which constitute, technically, the output layer of the CNN,but are herein depicted separately for emphasis). These outputs 606 flowas inputs into one or more neural networks 608 at the second level,which compute diastolic indicators 610, including, in accordance withvarious embodiments, one or more echocardiogram parameter estimates. Assymbolically indicated, the second-level neural network(s) 608 mayinclude multiple sub-levels, e.g., similar to the example architectures400, 500. (Further, although not explicitly shown, the neural network(s)608 may include multiple parallel branches of neural networks fordetermining multiple echocardiogram parameter estimates or otherdiastolic indicators.) However, in contrast to the computational modelarchitectures 400, 500, which take time-domain and time-frequencyfeatures as input, the neural networks 604, 608 operate on the raw ECGs602, and feature selection is performed implicitly as part of theapplication of the neural network(s) 608 at the second level. Morespecifically, as shown, inputs to the second-level neural network(s) 608may include the outputs 606 of the time-frequency transform implementedby the CNN 604 (all of these outputs constituting time-frequencyfeatures) as well as, optionally, the raw ECGs 602 and/or patientdemographic parameters 406. By virtue of network weight adjustments madeduring training, the second-level neural network(s) 608 may beconfigured to implicitly derive time-domain features from the ECG(s) 602and select or derive time-frequency features from the time-frequencytransform outputs 606 in one or more initial network layers, withsubsequent layers of the neural network(s) 608 operating on thesefeatures.

The neural network(s) 608 for computing the diastolic parameters may beimplemented by various types of neural networks known to those ofordinary skill in the art, which may be selected depending, e.g., on theparticular diastolic indicator. Neural networks suitable for computingechocardiogram parameter estimates and/or other diastolic indicatorsinclude, for example and without limitation, multi-layer perceptrons(MLPs) and probabilistic neural networks (PNNs) based on dynamic decayadjustment (DDA). MLPs can learn non-linear function approximators forclassification or regression, and may (in contrast to logisticregression) include one or more non-linear hidden layers between theinput and output layers. PNNs may be created using an algorithm, knownas “constructive training of PPNs,” that generates rules based onnumerical data, each rule defining a high-dimensional Gaussian functionthat is adjusted by two thresholds to avoid conflicts with rules ofdifferent classes.

The neural networks 604, 608 of the two-level neural networkarchitecture 600 may be trained in a supervised manner based on ECGs(and, optionally, patient demographic parameters) paired withground-truth echocardiogram parameters (as measured byechocardiography). The weights of the CNN 604 are initialized inaccordance with the desired time-frequency transform, e.g., to theselected wavelets of a CWT. The weights of the subsequent neuralnetwork(s) 608 may be initialized in multiple ways, e.g., with randomweights, and are adjusted during training. Training may also includemodifying the weights of the CNN 604. In principle, the combined,two-level neural network architecture 600 may be trained end-to-end fromthe initial weights, e.g., using back propagation (which is well-knownin the art) all the way through the CNN 604. It may be beneficial,however, to instead train the networks 604, 608 in two stages: In thefirst stage, the weights of the CNN 604 may be held fixed to provide thetime-frequency transform outputs 606 (e.g., wavelet coefficients) to beinput to the second-level neural network(s) 608, which may be trained,e.g., using back propagation stopping at the output of the CNN 604. Inthis manner, the second-level neural network(s) 608 can benefit from thespectral information provided by the time-frequency transform(s). Aftertraining the second-level neural-networks 608, the restriction of fixingthe weights of the CNN 604 at the first level can be relaxed, and thecombined two-level system of neural networks 604, 608 can be trained toadjust the weights at both levels.

FIGS. 7 and 8 are example user interfaces in accordance with variousembodiments, e.g., as may be displayed on a computing device (e.g.,desktop computer or tablet) utilized by a physician or other medicalprovider when evaluating a patient. The user interface in FIG. 7 showsan example report screen for an abnormal case, whereas the userinterface in FIG. 8 shows an example report screen for a borderlinecase. The report screens display, on the left side, the ECG traces andassociated time-frequency maps for multiple leads (e.g., as shown, leadsI, II, and III). The displayed leads may be selectable (e.g., among thestandard twelve leads) by the user (e.g., physician), using, forexample, drop-down lists accessible via user-interface features next tothe lead indicators. For the purpose of displaying the time-frequencymaps to the user, the unsigned version may be beneficial since it avoidspresenting potentially distracting information that is not of immediate,intuitively discernible clinical significance because the sign is notrelevant to an intuitive interpretation of the signal value of thetime-frequency map as a measure of the electrical energy of the heart.

On the right side of the example report screens, various quantitativeand qualitative metrics and indicators are displayed. These metrics mayinclude conventional ECG information, such as Glasgow parameters (e.g.,heart rate, various intervals measured between waveform features of theECG, etc.). In addition, various diastolic indicators derived, inaccordance with this disclosure, by machine-learned computational modelsfrom ECGs and time-frequency maps may be shown. For instance, acategorical indicator, e.g., displayed in the form of a segmented bar(optionally color-coded) with the applicable category being highlightedand the other categories being greyed out, may instantly inform thephysician of the overall diastolic health of the patient, e.g., whetherdiastolic function is normal, borderline (as in FIG. 8), or abnormal (asin FIG. 7). As will be appreciated, a five-segment bar may alternativelybe used for finer gradation. Alternatively or additionally, aquantitative risk score, which generally correlates with theclassification, may be indicated along a scale. For example, a high riskscore of about 6 may correspond to an abnormality, whereas a lower riskscore of about 3 may correspond to borderline diastolic function. Atextual risk statement indicating the type and/or likelihood ofabnormality may also be included. Further, to provide informationfamiliar to physicians from echocardiograms, the report screen may alsoprovide the computed echocardiogram parameter estimates, or indicesderived therefrom, e.g., as shown, lateral, septal, and composite LVrelaxation indices. Along with the numerical values, a graphicalrepresentation of likelihood ratios for the various indices may beprovided (e.g., by tick-marks along a scale, as shown). As can be seenfrom a comparison of FIGS. 7 and 8, abnormal diastolic function may bereflected in significantly higher values of the LV relaxation indices.Other graphic, numerical, and/or textual representations areconceivable. In general, the displayed information includes variousmeasures and indicators that may be useful for risk stratification.

To aid with the interpretation of the information displayed on thereport screen (or made available by other means), medical personnel mayalso be provided with an interpretation guide that presents statisticalmeasures taken from a reference study population. As an example, table 1below illustrates the kind of more detailed information that theinterpretation guide may contain to explain risk classifications (e.g.,into normal/borderline/abnormal), risk scores, and verbal riskassessments.

TABLE 1 Output Type Output Value Interpretation Summary Abnormal LVDDrelative risk: 12.1 Classification 95% CI [4.68, 31.11] p < 0.0001 LVRelaxation 5 Predicted average e' range: Risk Score 70% CI [5.68, 9.09]95% CI [3.97, 10.79] Risk Statement Probable 84.5% observed with LVDDIschemic or Structural AbnormalityAs another example, Table 2 provides various statistical measures forrelating LV relaxation indices to normal/borderline/abnormalclassifications:

TABLE 2 LV Relaxation Index Classification Statistical measures Lateral= 76 Abnormal Percentile (abnormal): 71% Percentile (normal): 4%Likelihood Ratio: 17.75 Septal = 68 Borderline Percentile (abnormal):45% Percentile (normal): 8% Likelihood Ratio: 5.63 Composite = 66Borderline Percentile (abnormal): 40% Percentile (normal): 3% LikelihoodRatio: 13.33As can be seen from this data, within the reference study population,among patients with a diastolic abnormality, 71% have a lateral index ofat least 76, but only 4% of patients with normal diastolic functionreach a lateral index that high, for a high likelihood ratio of 17.75;accordingly, a lateral index of 76 results in a classification asabnormal. By contrast, for a septal index of 68, the correspondingfractions of patients in the reference study population with abnormalversus normal diastolic function that reach or exceed that index valueare 45% and 8%, respectively, for a much lower likelihood ratio of 5.63;in this case, a classification as borderline results.

FIG. 9 is a block diagram of a machine in the example form of a computersystem 900 within which instructions for causing the machine to performany one or more of the methodologies discussed herein may be executed.In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Whileonly a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein. The example computer system900 includes one or more processors 902 (e.g., a central processing unit(CPU), a graphics processing unit (GPU) or both), a main memory 904 anda static memory 906, which communicate with each other via a bus 908.The computer system 900 may further include a video display unit 910(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). Thecomputer system 900 also includes an alphanumeric input device 912(e.g., a keyboard), a user interface (UI) navigation device 914 (e.g., amouse), a disk drive unit 916, a signal generation device 918 (e.g., aspeaker), a network interface device 920 to communicate via a network926, and a data interface device (such as, e.g., an electrodeinterface).

The disk drive unit 916 includes a machine-readable medium 922 storingone or more sets of instructions and data structures (e.g., software)924 embodying or utilized by any one or more of the methodologies orfunctions described herein. The instructions 924 may also reside,completely or at least partially, within the main memory 904 and/orwithin the processor 902 during execution thereof by the computer system900, the main memory 904 and the processor 902 also constitutingmachine-readable media.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding, or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; CD-ROM and DVD-ROM disks, or otherdata-storage devices. Further, the term “machine-readable medium” shallbe taken to include a non-tangible signal or transmission medium,including an electrical signal, a magnetic signal, an electromagneticsignal, an acoustic signal and an optical signal.

The following numbered examples are illustrative embodiments:

1. A method for quantifying diastolic function, the method comprising:receiving one or more electrocardiograms measured for a patient;converting the one or more electrocardiograms, using time-frequencytransform, into time-frequency features; operating one or moremachine-learned computational models on input comprising thetime-frequency features to compute an estimate or estimates of one ormore echocardiogram parameters indicative of diastolic function, the oneor more machine-learned computational models having been trained in asupervised manner using values of the one or more echocardiogramparameters obtained by echocardiography as ground-truth outputs.

2. The method of example 1, further comprising computing one or moreadditional indicators of diastolic function based at least in part onthe estimate or estimates of the one or more echocardiogram parameters.

3. The method of example 2, wherein the one or more additionalindicators of diastolic function are computed by operating one or moresecond machine-learned computational models on input comprising theestimate or estimates of the one or more echocardiogram parameters.

4. The method of example 3, wherein the one or more secondmachine-learned computational models comprise one or more ensemblemodels.

5. The method of example 2 or example 3, wherein the one or moreadditional indicators of diastolic function comprise at least one of aleft ventricular relaxation risk score, a lateral left ventricularrelaxation index, a septal left ventricular relaxation index, or acomposite left ventricular relaxation index.

6. The method of any of examples 2-5, wherein the one or more additionalindicators of diastolic function comprise a categorical diastolicfunction indicator.

7. The method of example 6, wherein the categorical diastolic indicatorhas a value range comprising normal, abnormal, and borderline diastolicfunction.

8. The method of example 6, wherein the categorical diastolic indicatorhas a value range comprising low-possibility, possible, borderline,probable, and highly probable left ventricular relaxation abnormality.

9. The method of any of examples 6-8, wherein the categorical diastolicfunction indicator is determined by comparison of the estimate orestimates of the one or more echocardiogram parameters against one ormore thresholds.

10. The method of any of examples 1-9, wherein the one or moremachine-learned computational models result from training on pairs ofinput feature sets and a ground-truth outputs for a plurality ofpatients, the input feature sets comprising the time-frequency features.

11. The method of any of examples 1-9, wherein a first neural network isused to convert the electrocardiograms into the time-frequency features,wherein the one or more machine-learned computational models compriseone or more second neural networks, and wherein the time-frequencyfeatures output by the first neural network are provided as inputs tothe one or more second neural networks.

12. The method of example 11, wherein weights of the first neuralnetwork are initialized to implement a time-frequency transform and aresubsequently adjusted during end-to-end training of the combined firstand second neural networks.

13. The method of example 12, wherein the one or more second neuralnetworks are trained with fixed values of the weights of the firstneural network prior to the end-to-end training of the combined firstand second neural networks.

14. The method of any of examples 1-13, wherein the one or morecomputational models comprise one or more regression models.

15. The method of example 14, wherein the one or more regression modelscomprise at least one of a random forest model or a least squares model.

16. The method of any of examples 1-15, wherein the time-frequencyfeatures derived from the time-frequency maps comprise extrema acrossfrequency at one or more points in time associated with the P, Q, R, S,or T waves.

17. The method of any of examples 1-16, wherein the input to the one ormore computational models further comprises at least one of one or morepatient demographic parameters or one or more time-domain featuresderived directly from the one or more electrocardiograms.

18. The method of example 17, wherein the one or more time-domainfeatures derived directly from the electrocardiograms compriseGlasgow-derived parameters.

19. A system comprising: one or more hardware processors; and memorystoring instructions which, when executed by the one or more hardwareprocessors, perform operations comprising: receiving one or moreelectrocardiograms measured for a patient; converting the one or moreelectrocardiograms, using time-frequency transform, into time-frequencyfeatures; operating one or more machine-learned computational models oninput comprising the time-frequency features to compute an estimate orestimates of one or more echocardiogram parameters indicative ofdiastolic function, the one or more machine-learned computational modelshaving been trained in a supervised manner using values of the one ormore echocardiogram parameters obtained by echocardiography asground-truth outputs.

20. The system of example 19, the operations further implementing any ofthe operations or limitations of examples 2-18.

21. A non-transitory computer-readable medium storingprocessor-executable instructions which, when executed by one or morecomputer processors, cause the one or more computer processors toperform operations comprising: receiving one or more electrocardiogramsmeasured for a patient; converting the one or more electrocardiograms,using time-frequency transform, into time-frequency features; operatingone or more machine-learned computational models on input comprising thetime-frequency features to compute an estimate or estimates of one ormore echocardiogram parameters indicative of diastolic function, the oneor more machine-learned computational models having been trained in asupervised manner using values of the one or more echocardiogramparameters obtained by echocardiography as ground-truth outputs.

22. The computer-readable medium of example 21, the operations furtherimplementing any of the operations or limitations of examples 2-18.

Although the invention has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

What is claimed is:
 1. A method for quantifying diastolic function, themethod comprising: receiving one or more electrocardiograms measured fora patient; converting the one or more electrocardiograms, usingtime-frequency transform, into time-frequency features; operating one ormore machine-learned computational models on input comprising thetime-frequency features to compute an estimate or estimates of one ormore echocardiogram parameters indicative of diastolic function, the oneor more machine-learned computational models having been trained in asupervised manner using values of the one or more echocardiogramparameters obtained by echocardiography as ground-truth outputs.
 2. Themethod of claim 1, further comprising computing one or more additionalindicators of diastolic function based at least in part on the estimateor estimates of the one or more echocardiogram parameters.
 3. The methodof claim 2, wherein the one or more additional indicators of diastolicfunction are computed by operating one or more second machine-learnedcomputational models on input comprising the estimate or estimates ofthe one or more echocardiogram parameters.
 4. The method of claim 3,wherein the one or more second machine-learned computational modelscomprise one or more ensemble models.
 5. The method of claim 2, whereinthe one or more additional indicators of diastolic function comprise atleast one of a left ventricular relaxation risk score, a lateral leftventricular relaxation index, a septal left ventricular relaxationindex, or a composite left ventricular relaxation index.
 6. The methodof claim 2, wherein the one or more additional indicators of diastolicfunction comprise a categorical diastolic function indicator.
 7. Themethod of claim 6, wherein the categorical diastolic indicator has avalue range comprising normal, abnormal, and borderline diastolicfunction.
 8. The method of claim 6, wherein the categorical diastolicindicator has a value range comprising low possibility, possible,borderline, probable, and highly probable left ventricular relaxationabnormality.
 9. The method of claim 6, wherein the categorical diastolicfunction indicator is determined by comparison of the estimate orestimates of the one or more echocardiogram parameters against one ormore thresholds.
 10. The method of claim 1, wherein the one or moremachine-learned computational models result from training on pairs ofinput feature sets and a ground-truth outputs for a plurality ofpatients, the input feature sets comprising the time-frequency features.11. The method of claim 1, wherein a first neural network is used toconvert the electrocardiograms into the time-frequency features, whereinthe one or more machine-learned computational models comprise one ormore second neural networks, and wherein the time-frequency featuresoutput by the first neural network are provided as inputs to the one ormore second neural networks.
 12. The method of claim 11, wherein weightsof the first neural network are initialized to implement atime-frequency transform and are subsequently adjusted during end-to-endtraining of the combined first and second neural networks.
 13. Themethod of claim 12, wherein the one or more second neural networks aretrained with fixed values of the weights of the first neural networkprior to the end-to-end training of the combined first and second neuralnetworks.
 14. The method of claim 1, wherein the one or morecomputational models comprise one or more regression models.
 15. Themethod of claim 14, wherein the one or more regression models compriseat least one of a random forest model or a least squares model.
 16. Themethod of claim 1, wherein the time-frequency features derived from thetime-frequency maps comprise extrema across frequency at one or morepoints in time associated with the P, Q, R, S, or T waves.
 17. Themethod of claim 1, wherein the input to the one or more computationalmodels further comprises at least one of one or more patient demographicparameters or one or more time-domain features derived directly from theone or more electrocardiograms.
 18. The method of claim 17, wherein theone or more time-domain features derived directly from theelectrocardiograms comprise Glasgow-derived parameters.
 19. A systemcomprising: one or more hardware processors; and memory storinginstructions which, when executed by the one or more hardwareprocessors, perform operations comprising: receiving one or moreelectrocardiograms measured for a patient; converting the one or moreelectrocardiograms, using time-frequency transform, into time-frequencyfeatures; operating one or more machine-learned computational models oninput comprising the time-frequency features to compute an estimate orestimates of one or more echocardiogram parameters indicative ofdiastolic function, the one or more machine-learned computational modelshaving been trained in a supervised manner using values of the one ormore echocardiogram parameters obtained by echocardiography asground-truth outputs.
 20. A non-transitory computer-readable mediumstoring processor-executable instructions which, when executed by one ormore computer processors, cause the one or more computer processors toperform operations comprising: receiving one or more electrocardiogramsmeasured for a patient; converting the one or more electrocardiograms,using time-frequency transform, into time-frequency features; operatingone or more machine-learned computational models on input comprising thetime-frequency features to compute an estimate or estimates of one ormore echocardiogram parameters indicative of diastolic function, the oneor more machine-learned computational models having been trained in asupervised manner using values of the one or more echocardiogramparameters obtained by echocardiography as ground-truth outputs.